Guide on Methodological Standards in Pharmacoepidemiology in China (2nd edition) and their series interpretation (19): tool systems and methodological paradigms for common data model-based data analysis

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Author: WU Yunxiao ^1, ² NIE Xiaolu ³ ZHENG Yongqi ^1, ² WANG Conghui ⁴ WU Jiarui ⁵ SUN Yexiang ⁶ SONG Haibo ⁷  ZHAN Siyan ^1, ^2, ^8, ⁹  SUN Feng ^1, ^2, ^8, ^10, ^11, ¹²

Affiliation: 1.Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China 2.Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China 3.Center for Clinical Epidemiology and Evidence-Based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China 4.Inner Mongolia Center for Drug Evaluation and Pharmacovigilance, Hohhot 010010, China 5.School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China 6.Yinzhou District Center for Disease Control and Prevention, Ningbo 315101, Zhejiang province, China 7.Drug Evaluation Center, National Medical Products Administration, NMPA/NMPA Center for Innovation and Research in Regulatory Science, Beijing 100163, China 8.Center of Postmarketing Drug Safety Evaluation, Peking University Health Science Center, Beijing 100191, China 9.Clinical Epidemiology Research Center, Peking University Third Hospital, Beijing 100191, China 10.Department of Ophthalmology, Peking University Third Hospital, Beijing 100191, China 11.College of Traditional Chinese Medicine, Xinjiang Medical University, Urumqi 830017, China 12.School of Public Health, Shihezi University, Shihezi 832000, Xinjiang Uygur Autonomous Region, China

Keywords: Pharmacoepidemiology Methodology Guideline Common data model Real world evidence

DOI: 10.12173/j.issn.1005-0698.202606128

Reference: Wu YX, Nie XL, Zheng YQ, et al. Guide on Methodological Standards in Pharmacoepidemiology in China (2nd edition) and their series interpretation (19): tool systems and methodological paradigms for common data model-based data analysis[J]. Chinese Journal of Pharmacoepidemiology, 2026, 35(7): 721-731. DOI: 10.12173/j.issn.1005-0698.202606128.[Article in Chinese]  Copied

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Abstract

With the expanding use of real-world data in post-marketing drug safety evaluation, comparative effectiveness research, drug utilization studies, and regulatory decision-making, differences in data structures, terminology mappings, data quality, and analytic workflows across heterogeneous data sources have become major challenges affecting the reproducibility of research and the credibility of evidence. The common data model (CDM) provides foundational support for real-world studies across institutions, regions, and countries by harmonizing data structures, standard terminology, and analytic interfaces. In recent years, tool ecosystems represented by OHDSI/HADES and the FDA Sentinel Initiative have promoted CDM-based data analysis from traditional single databases statistical modeling toward a standardized, reproducible, diagnosable, and distributable model of networked evidence generation. Based on the requirements for real-world data quality, study design, bias control, and statistical analysis plans outlined in the Guidelines for Pharmacoepidemiologic Research

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References

1. HennessyS, AtsutaY, HillS, et al. Real-world data and real-world evidence in regulatory decision making: report summary from the Council for International Organizations of Medical Sciences (CIOMS) Working Group XIII[J]. Pharmacoepidemiol Drug Saf, 2025, 34(3): e70117. DOI: 10.1002/pds.70117.

2. FinsterM, WenzelM, TaghizadehE. Common data models and data standards for tabular health data: a systematic review[J]. BMC Med Inform Decis Mak, 2025, 25(1): 422. DOI: 10.1186/s12911-025-03267-2.

3. VossEA, MakadiaR, MatchoA, et al. Feasibility and utility of applications of the common data model to multiple, disparate observational health databases[J]. J Am Med Inform Assoc, 2015, 22(3): 553-564. DOI: 10.1093/jamia/ocu023.

4. InitiativeSentinel. Sentinel Common Data Model[EB/OL] [2026-06-03]. https://www.sentinelinitiative.org/methods-data-tools/sentinel-common-data-model.

5. DARWINEU. Data Interoperability[EB/OL] [2026-06-03]. https://darwin-eu.org/index.php/methods/improving-interoperability.

6. SchuemieM, RepsJ, BlackA, et al. Health-analytics data to evidence suite (HADES): open-source software for observational research[J]. Stud Health Technol Inform, 2024, 310: 966-970. DOI: 10.3233/shti231108.

7. SchuemieMJ, RyanPB, PrattN, et al. Principles of large-scale evidence generation and evaluation across a network of databases (LEGEND)[J]. J Am Med Inform Assoc, 2020, 27(8): 1331-1337. DOI: 10.1093/jamia/ocaa103.

8. HripcsakG, DukeJD, ShahNH, et al. Observational health data sciences and informatics (OHDSI): opportunities for observational researchers[J]. Stud Health Technol Inform, 2015, 216: 574-578. https://pubmed.ncbi.nlm.nih.gov/26262116/.

9. the OHDSI CDM Working Group. OMOP Common Data Model[EB/OL]. [2026-06-04]. https://ohdsi.github.io/CommonDataModel/.

10. ReichC, OstropoletsA, RyanP, et al. OHDSI standardized vocabularies—a large-scale centralized reference ontology for international data harmonization[J]. J Am Med Inform Assoc, 2024, 31(3): 583-590. DOI: 10.1093/jamia/ocad247.

11. 国家药品监督管理局. 用于产生真实世界证据的真实世界数据指导原则(试行)[EB/OL] (2021-04-13)[2026-07-17]. https://www.cde.org.cn/main/news/viewInfoCommon/2a1c437ed54e7b838a7e86f4ac21c539.

12. 国家药品监督管理局. 药物真实世界研究设计与方案框架指导原则(试行)[EB/OL] (2023-02-17)[2026-07-17]. https://www.nmpa.gov.cn/xxgk/ggtg/ypggtg/ypqtggtg/20230217143016157.html?type=pc&m=.

13. BlacketerC, DefalcoFJ, RyanPB, et al. Increasing trust in real-world evidence through evaluation of observational data quality[J]. J Am Med Inform Assoc, 2021, 28(10): 2251-2257. DOI: 10.1093/jamia/ocab132.

14. YangJ, WuY, GuoJ, et al. Development and validation of identification algorithms for five autoimmune diseases using electronic health records: a retrospective cohort study in China[J]. Front Immunol, 2025, 16: 1541203. DOI: 10.3389/fimmu.2025. 1541203.

15. RaoGA, ShoaibiA, MakadiaR, et al. CohortDiagnostics: Phenotype evaluation across a network of observational data sources using population-level characterization[J]. PLoS One, 2025, 20(1): e0310634. DOI: 10.1371/journal.pone.0310634.

16. LundJL, RichardsonDB, StürmerT. The active comparator, new user study design in pharmacoepidemiology: historical foundations and contemporary application[J]. Curr Epidemiol Rep, 2015, 2(4): 221-228. DOI: 10.1007/s40471-015-0053-5.

17. SchneeweissS, RassenJA, GlynnRJ, et al. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data[J]. Epidemiology, 2009, 20(4): 512-522. DOI: 10.1097/EDE.0b013e3181a663cc.

18. TianY, SchuemieMJ, SuchardMA. Evaluating large-scale propensity score performance through real-world and synthetic data experiments[J]. Int J Epidemiol, 2018, 47(6): 2005-2014. DOI: 10.1093/ije/dyy120.

19. PetersenI, DouglasI, WhitakerH. Self controlled case series methods: an alternative to standard epidemiological study designs[J]. BMJ, 2016, 354: i4515. DOI: 10.1136/bmj.i4515.

20. NieX, XuL, BaiY, et al. Self-controlled case series design in vaccine safety: a systematic review[J]. Expert Rev Vaccines, 2022, 21(3): 313-324. DOI: 10.1080/14760584.2022.2020108.

21. SchuemieMJ, HripcsakG, RyanPB, et al. Robust empirical calibration of p-values using observational data[J]. Stat Med, 2016, 35(22): 3883-3888. DOI: 10.1002/sim.6977.

22. SchuemieMJ, HripcsakG, RyanPB, et al. Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data[J]. Proc Natl Acad Sci U S A, 2018, 115(11): 2571-2577. DOI: 10.1073/pnas.1708282114.

23. GagneJJ, HanX, HennessyS, et al. Successful comparison of US Food and Drug Administration Sentinel Analysis Tools to traditional approaches in quantifying a known drug-adverse event association[J]. Clin Pharmacol Ther, 2016, 100(5): 558-564. DOI: 10.1002/cpt.429.

24. KulldorffM, DavisRL, KolczakM, et al. A maximized sequential probability ratio test for drug and vaccine safety surveillance[J]. Sequential Analysis, 2011, 30(1): 58-78. DOI: 10.1080/07474946. 2011.539924.

25. YihWK, KulldorffM, SandhuSK, et al. Prospective influenza vaccine safety surveillance using fresh data in the Sentinel System[J]. Pharmacoepidemiol Drug Saf, 2016, 25(5): 481-492. DOI: 10.1002/pds.3908.

26. 孙一鑫, 王淼, 杨明芳, 等. 树状扫描统计量用于药品和疫苗安全性监测的综述[J]. 中华流行病学杂志, 2021, 42(7): 1286-1291.SunYX, WangM, YangMF, et al. Review on tree-based scan statistic in drug and vaccine safety monitoring[J]. Chinese Journal of Epidemiology, 2021, 42(7): 1286-1291. DOI: 10.3760/cma.j.cn112338-20201103-01297.

27. YihWK, MaroJC, NguyenM, et al. Assessment of quadrivalent human papillomavirus vaccine safety using the self-controlled tree-temporal scan statistic signal-detection method in the Sentinel System[J]. Am J Epidemiol, 2018, 187(6): 1269-1276. DOI: 10.1093/aje/kwy023.

28. ArlettP, KjaerJ, BroichK, et al. Real-world evidence in EU medicines regulation: enabling use and establishing value[J]. Clin Pharmacol Ther, 2022, 111(1): 21-23. DOI: 10.1002/cpt.2479.

29. WangSV, SreedharaSK, SchneeweissS, et al. Reproducibility of real-world evidence studies using clinical practice data to inform regulatory and coverage decisions[J]. Nat Commun, 2022, 13(1): 5126. DOI: 10.1038/s41467-022-32310-3.

30. PalchukMB, LondonJW, Perez-ReyD, et al. A global federated real-world data and analytics platform for research[J]. JAMIA Open, 2023, 6(2): ooad035. DOI: 10.1093/jamiaopen/ooad035.

31. HernánMA, RobinsJM. Using big data to emulate a target trial when a randomized trial is not available[J]. Am J Epidemiol, 2016, 183(8): 758-764. DOI: 10.1093/aje/kwv254.

32. HernánMA, SauerBC, Hernández-DíazS, et al. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses[J]. J Clin Epidemiol, 2016, 79: 70-75. DOI: 10.1016/j.jclinepi.2016.04.014.

33. CashinAG, HansfordHJ, HernánMA, et al. Transparent reporting of observational studies emulating a target trial—the TARGET Statement[J]. JAMA, 2025, 334(12): 1084-1093. DOI: 10.1001/jama.2025.13350.

34. SunF, BedenkovA, LiuBC, et al. Maximizing the value of real-world data and real-world evidence to accelerate healthcare transformation in China: summary of external advisory committee meetings[J]. Pharmaceut Med, 2024, 38(3): 157-166. DOI: 10.1007/s40290-024-00520-3.

35. XieJ, WuEQ, WangS, et al. Real-world data for healthcare research in China: call for actions[J]. Value Health Reg Issues, 2022, 27: 72-81. DOI: 10.1016/j.vhri.2021.05.002.

36. XuJ, WuW, ZhangX, et al. The use of real-world evidence for regulatory decisions in China[J]. Clin Pharmacol Ther, 2024, 116(1): 82-95. DOI: 10.1002/cpt.3257.

37. YuY, ZhengY, ZhangM, et al. Attitudes toward common data models among Chinese biomedical professionals: cross-sectional survey[J]. JMIR Med Inform, 2025, 13: e77603. DOI: 10.2196/77603.